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Speech enhancement in wireless acoustic sensor networks requires the exchange of audio signals. Since the wireless communication often dominates the nodes' energy budget, techniques for data exchange reduction are crucial. Adaptive quantization aims to optimize the bit depth of each exchanged signal according to its contribution to the speech enhancement performance. This enables the network to scale its energy and communication bandwidth requirements according to the current operating environment. The impact metric was previously proposed to predict the effect of quantization in linear minimum mean squared error (MMSE) estimation. We provide new insights into greedy adaptive quantization based on this impact metric. We achieve this by expanding the mathematical framework to include a new metric based on the gradient of the MMSE as a function of the quantization noise power. Using these tools, we show how the MMSE gradient naturally leads to a greedy algorithm and how the impact metric is a generalization of the gradient metric and a previously proposed metric. Besides, we validate the impact metric for adaptive quantization both in a simulated and in a real wireless acoustic sensor network deployed in a home environment, showing the energy savings achievable through greedy adaptive quantization.

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Distributed algorithms allow wireless acoustic sensor networks (WASNs) to divide the computational load of signal processing tasks, such as speech enhancement, among the sensor nodes. However, current algorithms focus on performance optimality, oblivious to the energy constraints that battery-powered sensor nodes usually face. To extend the lifetime of the network, nodes should be able to dynamically scale down their energy consumption when decreases in performance are tolerated. In this paper we study the relationship between energy and performance in the DANSE algorithm applied to speech enhancement. We propose two strategies that introduce flexibility to adjust the energy consumption and the desired performance. To analyze the impact of these strategies we combine an energy model with simulations. Results show that the energy consumption can be substantially reduced depending on the tolerated decrease in performance. This shows significant potential for extending the network lifetime using dynamic system reconfiguration.

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